CN103417228A - Method and system for automatically adjusting images - Google Patents

Method and system for automatically adjusting images Download PDF

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CN103417228A
CN103417228A CN2012101585051A CN201210158505A CN103417228A CN 103417228 A CN103417228 A CN 103417228A CN 2012101585051 A CN2012101585051 A CN 2012101585051A CN 201210158505 A CN201210158505 A CN 201210158505A CN 103417228 A CN103417228 A CN 103417228A
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image
static weight
weight
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CN103417228B (en
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曹宇宁
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Shanghai Huiying Medical Technology Co Ltd
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Beijing Sinopharm Hundric Medline Info Tec Co Ltd
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Abstract

The invention discloses a method and system for automatically adjusting images. Firstly, photometric weights are distributed in a static mode, secondly, photometric weights are adjusted in a dynamic mode, and accordingly the brightness of the images can be adjusted in a dynamic mode, the contrast ratio and the signal to noise ratio can be optimized, and the images will be more conveniently observed. Furthermore, compared with an existing automatic exposure method, the method for automatically adjusting the images is lower in cost, more precise in photometry, more flexible to control and very good in market prospect.

Description

A kind of image Automatic adjustment method and system
Technical field
The present invention relates to technical field of image processing, particularly a kind of image Automatic adjustment method and system.
Background technology
Along with the development of modern science and technology, the technological means that some are advanced and computer science and technology constantly are being applied in medical domain, and particularly, in radiodiagnosis and treatment subject, computer image processing technology is just being brought into play more and more important effect.
At present, for example, in the dynamic imaging system (: flat board is C greatly), for making picture quality, keep best, must adjust in real time exposure parameter.And the Control Technique for Automatic Exposure based on ionization chamber adopted in traditional X-ray quiescent imaging system can't meet above requirement.At first, ionization chamber system circuit complexity, response speed is slower; Secondly, its lower deployment cost is high, needs the design specialized hardware supported; Again, ionization chamber photometry sampled point is few, sampling area is large, can not accurately react the variation of image brightness distribution, and ionization chamber itself can the shield portions ray.
In view of this, prior art need to improve.
Summary of the invention
The object of the present invention is to provide a kind of image Automatic adjustment method and system, to solve, the response speed that in prior art, the Control Technique for Automatic Exposure based on ionization chamber exists is slow, the problems such as cost is high, low precision.
In order to achieve the above object, the present invention has taked following technical scheme:
A kind of image Automatic adjustment method wherein, said method comprising the steps of:
A1, the initial exposure amount is set, carries out image acquisition according to the initial exposure amount, and image is divided into to some zones;
The significance level of A2, the information that provides according to diverse location on image, selected the first module and the second module from described some zones, and set respectively static weight to described the first module and the second module, and the first module is set to high static weight, and the second module is set to low static weight;
A3, definite all the other regional static weight except first, second module, the value between the high static weight that described all the other regional static weight are the first module and the low static weight of the second module;
A4, calculate each area grayscale average, and according to the gray scale similarity to each region allocation changeable weight;
A5, changeable weight is carried out to normalized, and calculate each regional weighted mean;
A6, by regulating the initial exposure amount, make the error between described weighted mean and exposure target value be less than predictive error, realize that brightness of image regulates.
Described image Automatic adjustment method, wherein, before in described steps A 1, image being divided into to some zones, utilize transforming function transformation function to carry out greyscale transformation to image.
Described image Automatic adjustment method, wherein, described transforming function transformation function is logarithmic function or power function.
Described image Automatic adjustment method wherein, is divided into some zones by image and further comprises in described steps A 1:
Image is divided into to described some zones in the horizontal and vertical directions.
Described image Automatic adjustment method, wherein, described steps A 3 further comprises:
A31, selected curved surface prototype;
A32, on described curved surface the definition distance function;
A33, the surface equation when solving described distance function and getting minima, and, after bringing the coordinate in the zone except first, second module into, obtain the static weight in the zone except first, second module.
Described image Automatic adjustment method, wherein, described curved surface prototype is chosen to be polynomial surface, and described distance function is selected Euclidean distance d:
d ( a 0 , a 1 , . . . , a k ) = Σ m , n f ( m , n ) - Ws m , n
Wherein, a kBe every coefficient of surface equation expansion, (m, n) is the coordinate of having determined the zone of static weight, Ws M, nBe the static weight of the regional determined, f (m, n) calculates by fitting function, and above-mentioned k, m, n are natural numbers.
Described image Automatic adjustment method, wherein, described steps A 4 further comprises:
A41, using the first module as with reference to zone, calculate the manhatton distance of all the other each zone and the gray average of described reference zone;
A42, set a similarity function, by described similarity function, calculate each regional changeable weight.
8, image Automatic adjustment method according to claim 7, is characterized in that, the similarity function in described steps A 42 is gauss of distribution function.
Described image Automatic adjustment method, wherein, regulating the initial exposure amount in described steps A 6 is to adopt recursion filter to be regulated.
A kind of image automatic regulating system, wherein, described system comprises:
The preliminary treatment unit, for the initial exposure amount is set, carry out image acquisition according to the initial exposure amount, and image be divided into to some zones;
The first static weight allocation units, significance level for the information that provides according to diverse location on image, selected the first module and the second module from described some zones, and set respectively static weight to described the first module and the second module, the first module is set to high static weight, and the second module is set to low static weight;
The second static weight allocation units, for determining all the other the regional static weight except first, second module, the value between the high static weight that described all the other regional static weight are the first module and the low static weight of the second module;
The changeable weight allocation units, for calculating each area grayscale average, and according to the gray scale similarity to each region allocation changeable weight;
The normalization unit, for changeable weight is carried out to normalized, and calculate each regional weighted mean;
The image adjustment unit, for regulating the initial exposure amount, make the error between described weighted mean and exposure target value be less than predictive error, realizes the brightness of image adjusting.
Beneficial effect:
Image Automatic adjustment method provided by the invention and system, by first static allocation light metering weight, more dynamically adjust light metering weight; Thereby realized the dynamic adjustments brightness of image, optimized contrast and signal to noise ratio, made image more be beneficial to observation.In addition, with existing automatic explosion method, compare, image Automatic adjustment method cost of the present invention is lower, and photometry is more accurate, controls more flexibly, and market prospect is very good.
The accompanying drawing explanation
The flow chart that Fig. 1 is image Automatic adjustment method of the present invention.
The schematic diagram of the x-ray image that Fig. 2 is a lumbar vertebra normotopia.
The photometry subregion of the x-ray image that Fig. 3 is lumbar vertebra normotopia shown in Fig. 2 and weight distribution schematic diagram.
The structured flowchart that Fig. 4 is image automatic regulating system of the present invention.
The specific embodiment
For making purpose of the present invention, technical scheme and effect clearer, clear and definite, referring to the accompanying drawing examples, the present invention is described in more detail.
Refer to Fig. 1, its flow chart that is image Automatic adjustment method of the present invention.As shown in the figure, said method comprising the steps of:
S1, the initial exposure amount is set, carries out image acquisition according to the initial exposure amount, and image is divided into to some zones;
The significance level of S2, the information that provides according to diverse location on image, selected the first module and the second module from described some zones, and set respectively static weight to described the first module and the second module, and the first module is set to high static weight, and the second module is set to low static weight;
S3, definite all the other regional static weight except first, second module, the value between the high static weight that described all the other regional static weight are the first module and the low static weight of the second module;
S4, calculate each area grayscale average, and according to the gray scale similarity to each region allocation changeable weight;
S5, changeable weight is carried out to normalized, and calculate each regional weighted mean;
S6, by regulating the initial exposure amount, make the error between described weighted mean and exposure target value be less than predictive error, realize that brightness of image regulates.
Below for above-mentioned steps, be described in detail respectively.
Described step S1 comprises: an initial exposure amount is set, and carries out image acquisition according to the initial exposure amount, such as: in the dynamic imaging system, by operator rule of thumb, according to the diagnosis requirement, an applicable average somatotype patient's initial exposure amount is set, start to gather image.For example, the x-ray image that the image gathered is lumbar vertebra normotopia as shown in Figure 2.It should be noted that the purpose that the initial exposure amount is set is in order to accelerate regulation and control speed, reduce the suffered radiation of patient.Then carry out the photometry subregion, image is divided into to some zones, for following, zones of different is arranged to different static weight, for handled easily, can adopt the mode of decile to carry out the zone division to image.In the present embodiment, the x-ray image of described lumbar vertebra normotopia is divided in the horizontal and vertical directions to 16x16 zone.For the photometry subregion, usually guarantee that the interior pixel quantity in each zone is not less than 100 and gets final product.
When the image collected is not gray level image (such as color ultrasonic image etc.), should utilize transforming function transformation function to carry out greyscale transformation to image, adjust brightness of image, transforming function transformation function adopts logarithmic function or power function usually.As: power function:
Figure BSA00000720450200051
Or logarithmic function I '=klog γI; Wherein, k is proportional control factor, and γ is the gamma degree.
The significance level that described step S2 is the information that provides according to diverse location on image, selected the first module and the second module from described some zones, and set respectively static weight to described the first module and the second module, the first module is set to high static weight, and the second module is set to low static weight.There is larger difference in the significance level of the information that on image, diverse location, zones of different provide, and some zone can provide more information for diagnosis, and some regional information is not too important.Referring to Fig. 3, Fig. 3 is photometry subregion and the weight distribution schematic diagram of the normotopia of lumbar vertebra shown in Fig. 2 x-ray image, because diagnosis position (lumbar vertebra) concentrates on the image middle part, therefore near a plurality of zones residing image of lumbar vertebra central authorities are important area, these zones can provide important diagnostic message usually, image Automatic adjustment method of the present invention is defined as the first module by these important areas, the highest static weight is set, in the present embodiment, the static weight of the first module is set to peak 10.Near each zone image surrounding is because useful information is less, help to diagnosis is little, therefore by these the most unessential zone definitions, be the second module, lower static weight is set, in the present embodiment, the static weight of the second module is set to 1, but usually should not be made as 0 (minimum), in order to avoid black or excessively bright situation appearred in image, affect perception.For selecting of the first module and the second module, can be judged by clinician or other related personnel, and be completed by operates such as computers.
Described step S3 determines all the other regional static weight.In the present embodiment, the method that we adopt is: take the static weight of described the first module and the static weight of the second module is control point, by the Two-dimensional Surfaces matching, determine all the other the regional static weight except first, second module, the value between the high static weight that these all the other regional static weight are the first module and the low static weight of the second module.Specifically comprise the following steps:
S31, selected curved surface prototype, such as spline surface or polynomial surface etc., first determine a family of surfaces, then by following optimization method, selects one from family;
S32, on described curved surface a distance function of definition, take polynomial surface as example, distance is selected Euclidean distance, a kEvery coefficient of surface equation expansion,
d ( a 0 , a 1 , . . . , a k ) = Σ m , n f ( m , n ) - Ws m , n
Wherein, (m, n) is the coordinate of having determined the zone of static weight, Ws M, nBe exactly the static weight of definite regional, f (m, n) calculates by fitting function, and d is f (m, n) and Ws M, nEuclidean distance between the two, corresponding each coefficient a in the time of d value minimum 1To a kBe exactly optimized solution, above-mentioned k, m, n are natural numbers.
S33, the surface equation when solving described distance function and getting minima, and, after bringing all the other the regional coordinates except first, second module into, obtain the static weight in the zone except first, second module;
Be that above-mentioned Euclidean distance d gets minima, it must meet: ∂ d ∂ a 1 = 0 ∂ d ∂ a 2 = 0 . . . ∂ d ∂ a k = 0 . Separate above-mentioned equation and can try to achieve surface equation, each subregion coordinate of substitution can solve static weight Ws I, j=f (i, j), wherein, (i, j) is area coordinate, i, j are natural numbers, lower same.
After having determined the static weight of image regional, described step S4 is for according to practical situation dynamic assignment light metering weight: calculate each area grayscale average, and according to the gray scale similarity to each region allocation changeable weight.It specifically comprises:
S41, using the first module as with reference to zone, calculate the manhatton distance of all the other each zone and the gray average of described reference zone; The gray average when the first module is I 0, all the other each regional gray average I I, jGray average I with the first module 0Manhatton distance d I, j=| I I, j-I 0|.
S42, set a similarity function, by this similarity function, calculate each regional changeable weight.In the present embodiment, described similarity function is Gauss's partition function, changeable weight
Wd i , j = 1 2 π σ e - d i , j 2 2 σ 2
Wherein, σ is the standard deviation of Gauss distribution.
Described step S5 is the weighted mean after calculating normalization.At first weight is done to normalized, obtains the weight coefficient after normalization:
W i , j = Ws i , j × Wd i , j Σ ( Ws u , j × Wd i , j )
Then, calculate each regional weighted mean:
avgI = Σ i , j W i , j I i , j
Wherein, (i, j) is area coordinate.
Finally, described step S6 is by regulating the initial exposure amount, make the error between described weighted mean and exposure target value be less than predictive error, realize the brightness of image adjusting, it is adjusted to the final goal value by the gain of open loop regulating and controlling by brightness of image, thereby obtain the image of automatic exposure automatic gain, between brightness/contrast and signal to noise ratio, obtain optimum balance.When making error between described weighted mean and exposure target value be less than predictive error, can be controlled light value is adjusted in the range of error of desired value adnexa permission by PID controller (Proportional-Integral-Derivative Controller, proportional plus integral plus derivative controller), be met:
|avgI-T|≤Err
Wherein, T is exposure target value, and Err is the maximum error allowed.
In addition, while by the gain of open loop regulating and controlling, brightness of image being adjusted to the final goal value, gain:
Gain = I t arg et avgI
Wherein, described I TargetFor the final goal gray value, be according to the previously selected value of diagnostic requirements.The effect of Gain is to keep brightness of image, and medical field becomes ABS (Automatic Brightness Stabilization, auto brightness are controlled).
Further, for guaranteeing the flatness of regulating, regulate in described step S6 in the initial exposure amount, adopt iir filter (Infinite Impulse Response, recursion filter, infinite impulse response digital filter) to carry out the filtering adjusting, and control by the PID controller.The concrete grammar that IIR filtering is regulated is as follows:
expV 1＝W 0×expV 0+W 1×expV 1
Wherein, expV 0, expV 1Respectively the exposure value of last and current estimation, W 0, W 1Be weight coefficient, in image Automatic adjustment method of the present invention, auto-exposure control adopts closed loop control, and each circulation all can be calculated an expV, expV 0A upper circulation is calculated, expV 1Current circulation is calculated.W 0, W 1Span be [0,1], and sum of the two equals 1.
For said method, the present invention also provides a kind of image automatic regulating system, and as shown in Figure 4, described system comprises:
Preliminary treatment unit 100, for the initial exposure amount is set, carry out image acquisition according to the initial exposure amount, and image be divided into to some zones;
The first static weight allocation units 200, significance level for the information that provides according to diverse location on image, selected the first module and the second module from described some zones, and set respectively static weight to described the first module and the second module, the first module is set to high static weight, and the second module is set to low static weight;
The second static weight allocation units 300, for determining all the other the regional static weight except first, second module, the value between the high static weight that described all the other regional static weight are the first module and the low static weight of the second module;
Changeable weight allocation units 400, for calculating each area grayscale average, and according to the gray scale similarity to each region allocation changeable weight;
Normalization unit 500, for changeable weight is carried out to normalized, and calculate each regional weighted mean;
Image adjustment unit 600, for regulating the initial exposure amount, make the error between described weighted mean and exposure target value be less than predictive error, realizes the brightness of image adjusting.
The function of above-mentioned unit module is corresponding step S1-S6 respectively, can be with reference to the description in described method, and its specific implementation process has just repeated no more.
Further, described system can also comprise: image output unit, the image for the output image brightness regulation to the final goal value.
In sum, image Automatic adjustment method of the present invention and system, by first static allocation light metering weight, more dynamically adjust light metering weight; Thereby realized the dynamic adjustments brightness of image, optimized contrast and signal to noise ratio, made image more be beneficial to observation.In addition, with existing automatic explosion method, compare, image Automatic adjustment method cost of the present invention is lower, and photometry is more accurate, controls more flexibly, and market prospect is very good.
Be understandable that, for those of ordinary skills, can be equal to replacement or change according to technical scheme of the present invention and inventive concept thereof, and all these changes or replacement all should belong to the protection domain of the appended claim of the present invention.

Claims (10)

1. an image Automatic adjustment method, is characterized in that, said method comprising the steps of:
A1, the initial exposure amount is set, carries out image acquisition according to the initial exposure amount, and image is divided into to some zones;
The significance level of A2, the information that provides according to diverse location on image, selected the first module and the second module from described some zones, and set respectively static weight to described the first module and the second module, and the first module is set to high static weight, and the second module is set to low static weight;
A3, definite all the other regional static weight except first, second module, the value between the high static weight that described all the other regional static weight are the first module and the low static weight of the second module;
A4, calculate each area grayscale average, and according to the gray scale similarity to each region allocation changeable weight;
A5, changeable weight is carried out to normalized, and calculate each regional weighted mean;
A6, by regulating the initial exposure amount, make the error between described weighted mean and exposure target value be less than predictive error, realize that brightness of image regulates.
2. image Automatic adjustment method according to claim 1, is characterized in that, before in described steps A 1, image being divided into to some zones, utilizes transforming function transformation function to carry out greyscale transformation to image.
3. image Automatic adjustment method according to claim 2, is characterized in that, described transforming function transformation function is logarithmic function or power function.
4. image Automatic adjustment method according to claim 1, is characterized in that, in described steps A 1, image is divided into to some zones and further comprises:
Image is divided into to described some zones in the horizontal and vertical directions.
5. image Automatic adjustment method according to claim 1, is characterized in that, described steps A 3 further comprises:
A31, selected curved surface prototype;
A32, on described curved surface the definition distance function;
A33, the surface equation when solving described distance function and getting minima, and, after bringing the coordinate in the zone except first, second module into, obtain the static weight in the zone except first, second module.
6. image Automatic adjustment method according to claim 5, is characterized in that, described curved surface prototype is chosen to be polynomial surface, and described distance function is selected Euclidean distance d:
d ( a 0 , a 1 , . . . , a k ) = Σ m , n f ( m , n ) - Ws m , n
Wherein, a kBe every coefficient of surface equation expansion, (m, n) is the coordinate of having determined the zone of static weight, Ws M, nBe the static weight of the regional determined, f (m, n) calculates by fitting function, and above-mentioned k, m, n are natural numbers.
7. image Automatic adjustment method according to claim 1, is characterized in that, described steps A 4 further comprises:
A41, using the first module as with reference to zone, calculate the manhatton distance of all the other each zone and the gray average of described reference zone;
A42, set a similarity function, by described similarity function, calculate each regional changeable weight.
8. image Automatic adjustment method according to claim 7, is characterized in that, the similarity function in described steps A 42 is gauss of distribution function.
9. image Automatic adjustment method according to claim 1, is characterized in that, regulating the initial exposure amount in described steps A 6 is to adopt recursion filter to be regulated.
10. an image automatic regulating system, is characterized in that, described system comprises:
The preliminary treatment unit, for the initial exposure amount is set, carry out image acquisition according to the initial exposure amount, and image be divided into to some zones;
The first static weight allocation units, significance level for the information that provides according to diverse location on image, selected the first module and the second module from described some zones, and set respectively static weight to described the first module and the second module, the first module is set to high static weight, and the second module is set to low static weight;
The second static weight allocation units, for determining all the other the regional static weight except first, second module, the value between the high static weight that described all the other regional static weight are the first module and the low static weight of the second module;
The changeable weight allocation units, for calculating each area grayscale average, and according to the gray scale similarity to each region allocation changeable weight;
The normalization unit, for changeable weight is carried out to normalized, and calculate each regional weighted mean;
The image adjustment unit, for regulating the initial exposure amount, make the error between described weighted mean and exposure target value be less than predictive error, realizes the brightness of image adjusting.
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